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MiCrowd: Vision-Based Deep Crowd Counting on MCU

Cited 1 time in Web of Science Cited 2 time in Scopus
Authors

Son, Sungwook; Seo, Ahreum; Eo, Gyeongseon; Gill, Kwangyeon; Gong, Taesik; Kim, Hyung-Sin

Issue Date
2023-04
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Citation
Sensors, Vol.23 No.7
Abstract
Microcontrollers (MCUs) have been deployed on numerous IoT devices due to their compact sizes and low costs. MCUs are capable of capturing sensor data and processing them. However, due to their low computational power, applications processing sensor data with deep neural networks (DNNs) have been limited. In this paper, we propose MiCrowd, a floating population measurement system with a tiny DNNs running on MCUs since the data have essential value in urban planning and business. Moreover, MiCrowd addresses the following important challenges: (1) privacy issues, (2) communication costs, and (3) extreme resource constraints on MCUs. To tackle those challenges, we designed a lightweight crowd-counting deep neural network, named MiCrowdNet, which enables on-MCU inferences. In addition, our dataset is carefully chosen and completely re-labeled to train MiCrowdNet for counting people from an mobility view. Experiments show the effectiveness of MiCrowdNet and our relabeled dataset for accurate on-device crowd counting.
ISSN
1424-8220
URI
https://hdl.handle.net/10371/202133
DOI
https://doi.org/10.3390/s23073586
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  • Graduate School of Data Science
Research Area Distributed machine learning, Edge, Mobile AI

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